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fmas

Fundamentals of Multiagent Systems Textbook

fofc_benchmark

Fractional order flight control benchmark environment

followline

NXT

footbot_formation_control_tools

forecast_trajectories

Project developed using a multi-agents system with JADE for forecasting trajectories.

forest-wildfire-simulation

Forest wildfire simulation writtent in NetLogo using agents as firefighters and firesearchers

forma13

Code from FORMA'13 Workshop Sevilla 2013

formation-algorithm

Multi agent flocking and formation algorithm

formation-control

Decentralized Formation Tracking Control of Multiple Homogeneous Agents (M.S. THESIS)

formation-cotnrol-of-multi-agent-system

MATLAB based implementation of ofrmation control of multi-agent system

formation-python-si

Python training material for teachers in Engineering Sciences

formation_control

This is the repository for formation control with multi-agent systems implementing custom contour shapes and heterogenous swarms

formation_control-1

formation_control-2

formation_control_3d

3D formation control using consensus

formation_control_3d_quad

formation_feedback

formation_flight_sim

The simulation of formation control for quadrotor, including target allocation, global path planning and local path planning

founderfinder

Find team mates or founders in a dating like matching system (facebook/infect hackathon)

frc-2016-robot-java

Robot project for 2016 - Stronghold. Has multi robot, multi autonomous support.

free-programming-books

:books: Freely available programming books

free_fleet

A free fleet management system.

freekick3

One simple soccer game more

freertos

FreeRTOS

full-speed-jackal

ShanghaiTech University CS283 Fall 2017 Robotics Course Project

fundus-vessel-segmentation

One of the first steps in automatic fundus image analysis is the segmentation of the retinal vasculature, which provides valuable information related to several diseases. In this work, we present an extensive description and evaluation of our method for blood vessel segmentation in fundus images based on a discriminatively trained, fully connected conditional random field model. This task remains a challenge largely due to the desired structures being thin and elongated, a setting that performs particularly poorly using standard segmentation priors, such as a Potts model or total variation. We overcome this difficulty by using a conditional random field model with more expressive potentials, taking advantage of recent results enabling inference of fully connected models almost in real-time. Parameters of the method are learned automatically using a structured output support vector machine, a supervised technique widely used for structured prediction in a number of machine learning applications. The evaluation of our method is performed both quantitatively and qualitatively on DRIVE, STARE, CHASEDB1 and HRF, showing its ability to deal with different types of images and outperforming other techniques, trained using state of the art features.

fundus-vessel-segmentation-tmbe

One of the first steps in automatic fundus image analysis is the segmentation of the retinal vasculature, which provides valuable information related to several diseases. In this work, we present an extensive description and evaluation of our method for blood vessel segmentation in fundus images based on a discriminatively trained, fully connected conditional random field model. This task remains a challenge largely due to the desired structures being thin and elongated, a setting that performs particularly poorly using standard segmentation priors, such as a Potts model or total variation. We overcome this difficulty by using a conditional random field model with more expressive potentials, taking advantage of recent results enabling inference of fully connected models almost in real-time. Parameters of the method are learned automatically using a structured output support vector machine, a supervised technique widely used for structured prediction in a number of machine learning applications. The evaluation of our method is performed both quantitatively and qualitatively on DRIVE, STARE, CHASEDB1 and HRF, showing its ability to deal with different types of images and outperforming other techniques, trained using state of the art features.

fusiondepth

Official implementation for paper "Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR"

fuzzy-logic

Mobile robot simulation using VRep and Matlab's Fuzzy Logic toolbox

fuzzy-pid-dc-motor-matlab-simulink

Design of Fuzzy Logic + PID Controller for DC Motor using Matlab and Simulink